NLP_Examples.ipynb

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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: funcy in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (1.17)\n",
            "Requirement already satisfied: sklearn in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (0.0)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (0.16.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (2.11.3)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from wordcloud) (7.1.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->pyLDAvis) (2.0.1)\n"
          ]
        }
      ],
      "source": [
        "pip install keybert transformers bertopic multi_rake spacy gensim pyLDAvis wordcloud TextBlob vaderSentiment"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m spacy download en_core_web_lg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-QMuN2G0TG6O",
        "outputId": "2496adb4-1a2a-4227-8406-61292e753e85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting en_core_web_lg==2.2.5\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-2.2.5/en_core_web_lg-2.2.5.tar.gz (827.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 827.9 MB 1.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.7/dist-packages (from en_core_web_lg==2.2.5) (2.2.4)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (2.0.6)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.0)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (0.9.1)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.5)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (0.4.1)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (4.64.0)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.21.6)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.1.3)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (3.0.6)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (57.4.0)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (2.23.0)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (7.4.0)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.7)\n",
            "Requirement already satisfied: importlib-metadata>=0.20 in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (4.11.3)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (4.2.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.8.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (2022.5.18.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.0.4)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('en_core_web_lg')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "import pandas as pd\n",
        "import re"
      ],
      "metadata": {
        "id": "pSrkoRI2BrIY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "SauQFznfD1Fa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab038769-e746-45de-befc-e8f22f3eca6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/RedditFooddata.csv')\n",
        "#data = pd.read_csv('/content/drive/MyDrive/RedditEconomicdata.csv')"
      ],
      "metadata": {
        "id": "gWqRhuECNi4V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "documents = []\n",
        "for i in range(len(data)):\n",
        "    text = data['MsgBody'][i]\n",
        "    if len(text) > 4: # exclude comments with five or less than five words\n",
        "        documents.append(re.sub(\"[^a-zA-Z]+ |[^ ]+\\.[^ ]+ | [^ ]+/.[^ ]+ \", \"\", text))\n"
      ],
      "metadata": {
        "id": "CdB2QMecPxam"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "comment_str = ''\n",
        "for doc in documents:\n",
        "  comment_str += doc + ' '"
      ],
      "metadata": {
        "id": "eUYpzZ-5S0_m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n"
      ],
      "metadata": {
        "id": "F9nJ3mI-BrNL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "summarizer = pipeline(\"summarization\")\n",
        "summarized = summarizer(comment_str[:1000])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8H3vA7NCcqV",
        "outputId": "caa33490-ddbd-4a9d-8e70-52bb5124109e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 (https://huggingface.co/sshleifer/distilbart-cnn-12-6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(summarized)"
      ],
      "metadata": {
        "id": "50TVRrsfBrQK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "txtsentiment = pipeline(\"sentiment-analysis\", truncation=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJfLNfM_Qg0Y",
        "outputId": "cb9c005c-30a0-4d50-cf69-6152bf490ce8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "i = 0\n",
        "for doc in documents[:50]:\n",
        "  print(i, txtsentiment(doc))\n",
        "  i += 1 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JNkOQrzISQnO",
        "outputId": "91fd2e20-6b77-458e-a0df-0d6171e9915a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 [{'label': 'POSITIVE', 'score': 0.9990378618240356}]\n",
            "1 [{'label': 'POSITIVE', 'score': 0.9923766851425171}]\n",
            "2 [{'label': 'NEGATIVE', 'score': 0.9974379539489746}]\n",
            "3 [{'label': 'POSITIVE', 'score': 0.9922562837600708}]\n",
            "4 [{'label': 'NEGATIVE', 'score': 0.998417854309082}]\n",
            "5 [{'label': 'NEGATIVE', 'score': 0.9721264243125916}]\n",
            "6 [{'label': 'POSITIVE', 'score': 0.9793857336044312}]\n",
            "7 [{'label': 'NEGATIVE', 'score': 0.9987534284591675}]\n",
            "8 [{'label': 'NEGATIVE', 'score': 0.9996272325515747}]\n",
            "9 [{'label': 'NEGATIVE', 'score': 0.9919641017913818}]\n",
            "10 [{'label': 'NEGATIVE', 'score': 0.9536261558532715}]\n",
            "11 [{'label': 'POSITIVE', 'score': 0.9991777539253235}]\n",
            "12 [{'label': 'NEGATIVE', 'score': 0.992902934551239}]\n",
            "13 [{'label': 'NEGATIVE', 'score': 0.9843872785568237}]\n",
            "14 [{'label': 'NEGATIVE', 'score': 0.9979062080383301}]\n",
            "15 [{'label': 'NEGATIVE', 'score': 0.9982938170433044}]\n",
            "16 [{'label': 'NEGATIVE', 'score': 0.9982938170433044}]\n",
            "17 [{'label': 'NEGATIVE', 'score': 0.9996563196182251}]\n",
            "18 [{'label': 'POSITIVE', 'score': 0.9990599751472473}]\n",
            "19 [{'label': 'NEGATIVE', 'score': 0.9777395129203796}]\n",
            "20 [{'label': 'NEGATIVE', 'score': 0.9997771382331848}]\n",
            "21 [{'label': 'POSITIVE', 'score': 0.9998747110366821}]\n",
            "22 [{'label': 'NEGATIVE', 'score': 0.9982450008392334}]\n",
            "23 [{'label': 'POSITIVE', 'score': 0.9289413690567017}]\n",
            "24 [{'label': 'POSITIVE', 'score': 0.9998112320899963}]\n",
            "25 [{'label': 'NEGATIVE', 'score': 0.9913244247436523}]\n",
            "26 [{'label': 'NEGATIVE', 'score': 0.9989939332008362}]\n",
            "27 [{'label': 'POSITIVE', 'score': 0.9939334988594055}]\n",
            "28 [{'label': 'POSITIVE', 'score': 0.9998520612716675}]\n",
            "29 [{'label': 'NEGATIVE', 'score': 0.9876869320869446}]\n",
            "30 [{'label': 'POSITIVE', 'score': 0.9993731379508972}]\n",
            "31 [{'label': 'NEGATIVE', 'score': 0.9875164031982422}]\n",
            "32 [{'label': 'NEGATIVE', 'score': 0.9984796643257141}]\n",
            "33 [{'label': 'NEGATIVE', 'score': 0.9955064058303833}]\n",
            "34 [{'label': 'NEGATIVE', 'score': 0.9958816766738892}]\n",
            "35 [{'label': 'POSITIVE', 'score': 0.6579588055610657}]\n",
            "36 [{'label': 'POSITIVE', 'score': 0.9998176693916321}]\n",
            "37 [{'label': 'NEGATIVE', 'score': 0.9951605200767517}]\n",
            "38 [{'label': 'NEGATIVE', 'score': 0.9962059259414673}]\n",
            "39 [{'label': 'POSITIVE', 'score': 0.5672533512115479}]\n",
            "40 [{'label': 'NEGATIVE', 'score': 0.9977130889892578}]\n",
            "41 [{'label': 'NEGATIVE', 'score': 0.963648796081543}]\n",
            "42 [{'label': 'POSITIVE', 'score': 0.9997797608375549}]\n",
            "43 [{'label': 'NEGATIVE', 'score': 0.9665584564208984}]\n",
            "44 [{'label': 'NEGATIVE', 'score': 0.9979453682899475}]\n",
            "45 [{'label': 'NEGATIVE', 'score': 0.9989238381385803}]\n",
            "46 [{'label': 'NEGATIVE', 'score': 0.9926203489303589}]\n",
            "47 [{'label': 'NEGATIVE', 'score': 0.8401222229003906}]\n",
            "48 [{'label': 'NEGATIVE', 'score': 0.9449499249458313}]\n",
            "49 [{'label': 'NEGATIVE', 'score': 0.95201176404953}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "txtclsfcn_pipe = pipeline(\"text-classification\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "09bO13L-SQqE",
        "outputId": "319d7a18-0fdf-4421-83d1-a89f7e9351bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for doc in documents[:50]:\n",
        "  print(txtclsfcn_pipe(doc))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fQg0ACDUS09B",
        "outputId": "676637d1-b1a3-4891-c494-5126a9cbf906"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'label': 'POSITIVE', 'score': 0.9990378618240356}]\n",
            "[{'label': 'POSITIVE', 'score': 0.9923766851425171}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9974379539489746}]\n",
            "[{'label': 'POSITIVE', 'score': 0.9922562837600708}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.998417854309082}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9721264243125916}]\n",
            "[{'label': 'POSITIVE', 'score': 0.9793857336044312}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9987534284591675}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9996272325515747}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9919641017913818}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9536261558532715}]\n",
            "[{'label': 'POSITIVE', 'score': 0.9991777539253235}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.992902934551239}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9843872785568237}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9979062080383301}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9982938170433044}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9982938170433044}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9996563196182251}]\n",
            "[{'label': 'POSITIVE', 'score': 0.9990599751472473}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9777395129203796}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9997771382331848}]\n",
            "[{'label': 'POSITIVE', 'score': 0.9998747110366821}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9982450008392334}]\n",
            "[{'label': 'POSITIVE', 'score': 0.9289413690567017}]\n",
            "[{'label': 'POSITIVE', 'score': 0.9998112320899963}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9913244247436523}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9989939332008362}]\n",
            "[{'label': 'POSITIVE', 'score': 0.9939334988594055}]\n",
            "[{'label': 'POSITIVE', 'score': 0.9998520612716675}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9876869320869446}]\n",
            "[{'label': 'POSITIVE', 'score': 0.9993731379508972}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9875164031982422}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9984796643257141}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9955064058303833}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9958816766738892}]\n",
            "[{'label': 'POSITIVE', 'score': 0.6579588055610657}]\n",
            "[{'label': 'POSITIVE', 'score': 0.9998176693916321}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9951605200767517}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9962059259414673}]\n",
            "[{'label': 'POSITIVE', 'score': 0.5672533512115479}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9977130889892578}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.963648796081543}]\n",
            "[{'label': 'POSITIVE', 'score': 0.9997797608375549}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9665584564208984}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9979453682899475}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9989238381385803}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9926203489303589}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.8401222229003906}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9449499249458313}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.95201176404953}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(txtclsfcn_pipe(comment_str[:512]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ea8ljTS8Xqmv",
        "outputId": "4d5affd3-58c6-4f81-e924-08bfdcea4bfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'label': 'NEGATIVE', 'score': 0.9853014349937439}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keybert import KeyBERT\n",
        "\n",
        "kw_model = KeyBERT()\n",
        "keywords = kw_model.extract_keywords(comment_str)"
      ],
      "metadata": {
        "id": "1CfKbLcIb_Ye"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(keywords)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kJOiPLKgb_gc",
        "outputId": "6bdf02de-95e0-4a95-9bda-c57ce10d24b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('meatscooked', 0.46), ('mediumishcooked', 0.4582), ('fooddelicious', 0.4516), ('sauceprecooked', 0.4423), ('tastydefinitely', 0.435)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kw_model.extract_keywords(comment_str, keyphrase_ngram_range=(1, 1), stop_words=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qZxEMv50cu0n",
        "outputId": "b6966c6b-ad55-4c6f-d9b0-d3064289be2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('meatscooked', 0.46),\n",
              " ('mediumishcooked', 0.4582),\n",
              " ('fooddelicious', 0.4516),\n",
              " ('sauceprecooked', 0.4423),\n",
              " ('tastydefinitely', 0.435)]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kw_model.extract_keywords(comment_str, keyphrase_ngram_range=(1, 2), stop_words=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i3JSkRChcu3Z",
        "outputId": "48b8b45a-3b60-48cd-b595-38c07c14b07e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('ribeyes mediumishcooked', 0.5231),\n",
              " ('food colour', 0.5026),\n",
              " ('fried ribeye', 0.4988),\n",
              " ('food colouring', 0.4928),\n",
              " ('rib meat', 0.491)]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from bertopic import BERTopic"
      ],
      "metadata": {
        "id": "uBN6D9w65sBQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "topic_model = BERTopic()\n",
        "topics, probs = topic_model.fit_transform(documents)"
      ],
      "metadata": {
        "id": "1eZojhA65shN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d87f66b-0b3e-4ea1-fed9-32d540c07e57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numba/np/ufunc/parallel.py:363: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
            "  warnings.warn(problem)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "topic_model.get_topic_info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "64WX3Fr651yX",
        "outputId": "557e9e75-5793-4b36-e2ac-39dc730d3f2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Topic  Count                                               Name\n",
              "0       -1   2542                                 -1_it_and_that_the\n",
              "1        0    259                        0_canada_drive_ontario_trip\n",
              "2        1    124                          1_looks_amazing_good_that\n",
              "3        2    104                                       2_removed___\n",
              "4        3    101                      3_poutine_quebec_fries_cheese\n",
              "..     ...    ...                                                ...\n",
              "147    146     11              146_weather_comforting_lifeyou_snowed\n",
              "148    147     11  147_imgemotet52qh551792_imgemotet52qh551792loo...\n",
              "149    148     11                  148_syrup_youreal_diverse_brussel\n",
              "150    149     11                     149_thank_courage_indeed_above\n",
              "151    150     10                  150_italian_cultural_swiss_french\n",
              "\n",
              "[152 rows x 3 columns]"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "topic_model.get_topic(0)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BteiTQmwCrEH",
        "outputId": "dbcfc486-73c5-42ff-c40c-ac504b8f792b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('canada', 0.02120557602063367),\n",
              " ('drive', 0.020837627559547312),\n",
              " ('ontario', 0.019746389300418537),\n",
              " ('trip', 0.01630906542598834),\n",
              " ('place', 0.013395472071464302),\n",
              " ('from', 0.013262444102394381),\n",
              " ('away', 0.011845331877736585),\n",
              " ('toronto', 0.011638089792722716),\n",
              " ('live', 0.011303668590657826),\n",
              " ('detroit', 0.011059103294083472)]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "topic_model.visualize_topics()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667
        },
        "id": "htSzVqxi511M",
        "outputId": "b2f2a44c-ad9b-4375-c1a4-f940a3f555b6"
      },
      "execution_count": null,
      "outputs": [
        {
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              "var x = new MutationObserver(function (mutations, observer) {{\n",
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              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "\n",
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              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "topic_model.visualize_barchart()"
      ],
      "metadata": {
        "id": "8m8jK8hhC1hJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from multi_rake import Rake\n",
        "rake = Rake()\n",
        "keywords = rake.apply(comment_str)\n",
        "print(keywords[:10])\n"
      ],
      "metadata": {
        "id": "kCUn-K69N7La"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "import numpy as np\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.corpus import stopwords"
      ],
      "metadata": {
        "id": "DaLLiRBcbH-U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lemmatizer = WordNetLemmatizer()"
      ],
      "metadata": {
        "id": "AzPV2LInbIBH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('stopwords')\n",
        "stop_words = set(stopwords.words('english'))\n",
        "print(stop_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-rCi57PbIDe",
        "outputId": "b39a90a4-98f2-45ff-c6cd-cf5a44182aaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "{'off', 'its', 'before', 'now', 'yourself', \"hadn't\", 'll', 'were', 'been', 'own', 'will', \"you'd\", 'themselves', 'wasn', 'any', 'your', 'such', 'why', 'while', 'down', 'only', 'these', 'between', \"don't\", \"needn't\", 'against', 'out', 'over', 'yours', 'after', 't', 'me', \"you've\", 'very', 'who', 'we', 'which', 'but', 'because', 'that', 'each', 'i', \"aren't\", 'my', 'further', \"won't\", 'during', 'them', 'myself', 'more', \"shouldn't\", 'so', 'having', 'than', 'itself', 'can', 'as', 'him', 'the', \"isn't\", 'it', 'weren', 'am', 'nor', 'where', 'yourselves', 'm', 'do', 'into', 'for', 'had', 'up', 'our', 'being', 'does', \"hasn't\", 'd', 'was', 'until', \"weren't\", 'from', 'below', 's', 'haven', 'there', 'is', 'same', 'hadn', \"haven't\", 'doesn', \"wouldn't\", 'needn', \"mustn't\", 'an', 'if', 'ours', 'some', 'at', 'you', \"mightn't\", 'he', 'just', 'above', 'too', 'has', 'aren', 'mustn', 'shan', 'o', 'or', 'hers', 'all', 'wouldn', 'both', 'y', 'when', 'don', 'in', 'no', \"you're\", \"wasn't\", \"that'll\", \"you'll\", \"doesn't\", 'won', 'with', 'by', 'once', 'most', 'shouldn', 'doing', 'to', 'be', 'their', 'not', 'what', 'on', 'here', 'couldn', 'isn', 'ourselves', 'she', 'about', 'ma', \"it's\", 'few', 'and', 'mightn', 'again', \"she's\", 'himself', 'didn', 'they', 'those', \"should've\", 'hasn', 'herself', 'did', 'other', 're', 'then', 'should', 'how', 'her', 'of', 'his', 'under', \"shan't\", 'whom', 'through', 'have', \"couldn't\", 've', 'theirs', 'a', \"didn't\", 'are', 'ain', 'this'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DnzS9jcaeauG",
        "outputId": "15d68ad9-68f7-400a-c0e6-51b3bef0759d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def text_tokenize(texttoken):\n",
        "    txt = texttoken\n",
        "    txt = txt.lower()\n",
        "    tokens = nltk.tokenize.word_tokenize(txt)\n",
        "    tokens = [t for t in tokens if len(t) > 2]\n",
        "    tokens = [lemmatizer.lemmatize(t) for t in tokens]\n",
        "    tokens = [t for t in tokens if t not in stop_words]\n",
        "    return tokens"
      ],
      "metadata": {
        "id": "Q8Jx9AZFcWTy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf_doc = []\n",
        "for doc in documents:\n",
        "  tfidf_doc.append(doc)"
      ],
      "metadata": {
        "id": "KQpL1rKlgeqU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "vectorizer = TfidfVectorizer()\n",
        "tfidf_vec = vectorizer.fit_transform(tfidf_doc)"
      ],
      "metadata": {
        "id": "l-RF9ELxXJ7b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vectorizer.get_feature_names_out()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mQMs7UwCXJ-U",
        "outputId": "345bea04-fda1-485e-a853-f8af395db460"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['0325', '05', '0jncztdhu8o', ..., 'çok', 'الكنافة', '油条'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(pd.DataFrame(tfidf_vec.toarray()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I9Mvf6SjXKA_",
        "outputId": "09eab382-5055-4334-bc1e-84c497cfbef1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      0      1      2      3      4      5      6      7      8      9      \\\n",
            "0       0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "1       0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "2       0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "3       0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "4       0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "...     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
            "7519    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "7520    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "7521    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "7522    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "7523    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "\n",
            "      ...  14531  14532  14533  14534  14535  14536  14537  14538  14539  \\\n",
            "0     ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "1     ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "2     ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "3     ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "4     ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "...   ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
            "7519  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "7520  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "7521  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "7522  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "7523  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
            "\n",
            "      14540  \n",
            "0       0.0  \n",
            "1       0.0  \n",
            "2       0.0  \n",
            "3       0.0  \n",
            "4       0.0  \n",
            "...     ...  \n",
            "7519    0.0  \n",
            "7520    0.0  \n",
            "7521    0.0  \n",
            "7522    0.0  \n",
            "7523    0.0  \n",
            "\n",
            "[7524 rows x 14541 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "from gensim import corpora\n",
        "from gensim import models\n",
        "import pyLDAvis\n",
        "import pyLDAvis.gensim_models\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "BjMiGP4guYRs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e32d5ee9-100a-4ce6-9154-534716940b05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/past/types/oldstr.py:5: DeprecationWarning:\n",
            "\n",
            "Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nlp = spacy.load('en_core_web_lg')\n"
      ],
      "metadata": {
        "id": "hJAE_m3rtwou"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "doc_spacy_tokens = []\n",
        "for document in documents: \n",
        "    doc_tokens = []\n",
        "    for token in nlp(document):\n",
        "        if((token.is_stop is False) or (len(token) > 2) or (nlp.vocab.has_vector(str(token)))):\n",
        "          doc_tokens.append(str(token.lemma_))\n",
        "    doc_spacy_tokens.append(doc_tokens)"
      ],
      "metadata": {
        "id": "ueQDS8lBXPPQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "doc_dictionary = corpora.Dictionary(doc_spacy_tokens)\n",
        "doc_corpus = [doc_dictionary.doc2bow(sentence) for sentence in doc_spacy_tokens] \n",
        "tfidf = models.TfidfModel(doc_corpus)\n",
        "doc_corpus_tfidf = tfidf[doc_corpus]"
      ],
      "metadata": {
        "id": "RrmpumL2XPSA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "no_topics = 10\n",
        "lda_model = models.ldamodel.LdaModel(doc_corpus_tfidf, id2word=doc_dictionary, num_topics=no_topics)\n"
      ],
      "metadata": {
        "id": "28BbMv_hXPVI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for topic in lda_model.show_topics(num_topics=no_topics, num_words=10):\n",
        "    print(topic) \n",
        "\n",
        "for topic_proportion in lda_model[doc_corpus_tfidf[0]]: \n",
        "    print(topic_proportion)\n"
      ],
      "metadata": {
        "id": "nDzz4wCYZW7r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lda_model.print_topics(num_topics=no_topics, num_words=10)"
      ],
      "metadata": {
        "id": "_BCIG85TZW-T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lda_visualization = pyLDAvis.gensim_models.prepare(lda_model, doc_corpus_tfidf, doc_dictionary)\n",
        "pyLDAvis.save_html(lda_visualization, '/content/drive/MyDrive/redditeconomics_lda_result.html')\n"
      ],
      "metadata": {
        "id": "ksivFgeoZrIf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from gensim.models import CoherenceModel\n",
        "coherence_model_lda = CoherenceModel(model=lda_model, texts=doc_spacy_tokens, dictionary=doc_dictionary, coherence='c_v') # by changing the values for the \"coherence\" parameter, you can use different coherence methods (e.g., 'u_mass', 'c_v', 'c_uci', 'c_npmi')\n",
        "coherence_lda = coherence_model_lda.get_coherence()\n",
        "print(coherence_lda)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jW-16Po4Zq9U",
        "outputId": "a6184660-1b82-49a2-e210-6afc4a6d38b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.3865748720432256\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "top_topics = lda_model.top_topics(doc_corpus) \n",
        "avg_topic_coherence = sum([t[1] for t in top_topics]) / no_topics\n",
        "print('Average topic coherence: %.4f.' % avg_topic_coherence)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vVZYwsXoZrJW",
        "outputId": "701af687-da36-4d39-a523-a2ece17381c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average topic coherence: -5.1202.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from wordcloud import WordCloud"
      ],
      "metadata": {
        "id": "Zwg1rjE6bRtc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract words from each top topic for wordlcoud\n",
        "topic_words = ''\n",
        "for i in range(no_topics):\n",
        "    for j in range(10):\n",
        "        topic_words += top_topics[i][0][j][1] + ' '"
      ],
      "metadata": {
        "id": "E3cS_bwqbRv-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wordcloud = WordCloud(background_color ='white').generate(topic_words)                      \n",
        "plt.figure(figsize = (8, 8), facecolor = None)\n",
        "plt.imshow(wordcloud)\n",
        "plt.axis(\"off\")\n",
        "plt.tight_layout(pad = 0)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "P6yNkjazbdbs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from textblob import TextBlob\n",
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer"
      ],
      "metadata": {
        "id": "BdeoiXqkbdeV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "words = TextBlob(topic_words)\n",
        "print(words.sentiment)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p_yQaarOb3JF",
        "outputId": "94ac0782-41d6-4465-aea5-09da57a6d411"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentiment(polarity=0.7642857142857143, subjectivity=0.7928571428571429)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "analyzer = SentimentIntensityAnalyzer()\n",
        "print(analyzer.polarity_scores(topic_words))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bXUr4yzkb3ME",
        "outputId": "aff623f5-1aab-426f-c385-02bf64329ead"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'neg': 0.014, 'neu': 0.702, 'pos': 0.284, 'compound': 0.9893}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for document in documents[:50]:\n",
        "  doc = nlp(document)\n",
        "  for token in doc:\n",
        "    print(token.text, token.pos_, token.tag_, token.is_stop)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iyXYuUszvg9N",
        "outputId": "051dcad9-69c9-4483-8726-e65404871ace"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This DET DT True\n",
            "is AUX VBZ True\n",
            "a DET DT True\n",
            "* PUNCT NFP False\n",
            "brilliantlydefensive ADJ JJ False\n",
            "title NOUN NN False\n",
            ". PUNCT . False\n",
            "I PRON PRP True\n",
            "like VERB VBP False\n",
            "it PRON PRP True\n",
            "black ADJ JJ False\n",
            "and CCONJ CC True\n",
            "blue ADJ JJ False\n",
            "once ADV RB True\n",
            "in ADP IN True\n",
            "a DET DT True\n",
            "whileNot PROPN NNP False\n",
            "so ADV RB True\n",
            "much ADV RB True\n",
            "a DET DT True\n",
            "matter NOUN NN False\n",
            "of ADP IN True\n",
            "taste NOUN NN False\n",
            "but CCONJ CC True\n",
            "a DET DT True\n",
            "matter NOUN NN False\n",
            "of ADP IN True\n",
            "mood NOUN NN False\n",
            "for ADP IN True\n",
            "me PRON PRP True\n",
            ". PUNCT . False\n",
            "That SCONJ IN True\n",
            "what PRON WP True\n",
            "I PRON PRP True\n",
            "was AUX VBD True\n",
            "thinking VERB VBG False\n",
            "how ADV WRB True\n",
            "long ADV RB False\n",
            "per ADP IN True\n",
            "sideminutes NOUN NNS False\n",
            "? PUNCT . False\n",
            "Did AUX VBD True\n",
            "you PRON PRP True\n",
            "taste VERB VB False\n",
            "it PRON PRP True\n",
            "? PUNCT . False\n",
            "Fucking INTJ UH False\n",
            "A NOUN NN True\n",
            "! PUNCT . False\n",
            "A DET DT True\n",
            "truly ADV RB False\n",
            "disturbing ADJ JJ False\n",
            "amount NOUN NN True\n",
            "of ADP IN True\n",
            "mayo NOUN NN False\n",
            "I PRON PRP True\n",
            "miss VERB VBP False\n",
            "Orange PROPN NNP False\n",
            "County PROPN NNP False\n",
            "Asian ADJ JJ False\n",
            "food NOUN NN False\n",
            "Now ADV RB True\n",
            "that SCONJ IN True\n",
            "you PRON PRP True\n",
            "said VERB VBD False\n",
            "something PRON NN True\n",
            "imma VERB VB False\n",
            "mess NOUN NN False\n",
            "it PRON PRP True\n",
            "up ADP RP True\n",
            "next ADJ JJ True\n",
            "time NOUN NN False\n",
            "xDI PROPN NNP False\n",
            "just ADV RB True\n",
            "treated VERB VBD False\n",
            "it PRON PRP True\n",
            "like SCONJ IN False\n",
            "a DET DT True\n",
            "charcoal NOUN NN False\n",
            "fire NOUN NN False\n",
            ". PUNCT . False\n",
            "Just ADV RB True\n",
            "like SCONJ IN False\n",
            "the DET DT True\n",
            "time NOUN NN False\n",
            "I PRON PRP True\n",
            "could VERB MD True\n",
            "ve VERB VB False\n",
            "met VERB VBD False\n",
            "MrTat PROPN NNP False\n",
            "the DET DT True\n",
            "mallThe PROPN NNP False\n",
            "entire ADJ JJ False\n",
            "day NOUN NN False\n",
            "I PRON PRP True\n",
            "kept VERB VBD False\n",
            "saying VERB VBG False\n",
            "Ill PROPN NNP False\n",
            "go VERB VB True\n",
            "a DET DT True\n",
            "little ADJ JJ False\n",
            "laterIll PROPN NNP False\n",
            "go VERB VB True\n",
            "a DET DT True\n",
            "little ADJ JJ False\n",
            "laterAnd INTJ UH False\n",
            "then ADV RB True\n",
            "when ADV WRB True\n",
            "I PRON PRP True\n",
            "got VERB VBD False\n",
            "there ADV RB True\n",
            "they PRON PRP True\n",
            "told VERB VBD False\n",
            "me PRON PRP True\n",
            "he PRON PRP True\n",
            "just ADV RB True\n",
            "leftAnd NUM CD False\n",
            "when ADV WRB True\n",
            "I PRON PRP True\n",
            "asked VERB VBD False\n",
            "a DET DT True\n",
            "mall NOUN NN False\n",
            "guy NOUN NN False\n",
            "if SCONJ IN True\n",
            "he PRON PRP True\n",
            "would VERB MD True\n",
            "ever ADV RB True\n",
            "come VERB VB False\n",
            "back ADV RB True\n",
            "again ADV RB True\n",
            "he PRON PRP True\n",
            "said VERB VBD False\n",
            "he PRON PRP True\n",
            "did AUX VBD True\n",
            "nt PART RB False\n",
            "know VERB VB False\n",
            ". PUNCT . False\n",
            "Its DET PRP$ True\n",
            "ribsTotally ADV RB False\n",
            "different ADJ JJ False\n",
            "cut NOUN NN False\n",
            "demanding VERB VBG False\n",
            "a DET DT True\n",
            "totally ADV RB False\n",
            "differentThat ADJ JJ False\n",
            "was AUX VBD True\n",
            "cooked VERB VBN False\n",
            "low ADJ JJ False\n",
            "and CCONJ CC True\n",
            "slow ADJ JJ False\n",
            "as SCONJ IN True\n",
            "ribs NOUN NNS False\n",
            "should VERB MD True\n",
            "beTheres NOUN NNS False\n",
            "no DET DT True\n",
            "comparison NOUN NN False\n",
            "to PART TO True\n",
            "be AUX VB True\n",
            "made VERB VBN True\n",
            "hereIts PROPN NNP False\n",
            "apples NOUN NNS False\n",
            "and CCONJ CC True\n",
            "oranges NOUN NNS False\n",
            "but CCONJ CC True\n",
            "with ADP IN True\n",
            "beef NOUN NN False\n",
            ". PUNCT . False\n",
            "A DET DT True\n",
            "food NOUN NN False\n",
            "designed VERB VBN False\n",
            "solely ADV RB False\n",
            "to PART TO True\n",
            "be AUX VB True\n",
            "posted VERB VBN False\n",
            "online ADV RB False\n",
            "Of ADV RB True\n",
            "course ADV RB False\n",
            "it PRON PRP True\n",
            "has AUX VBZ True\n",
            "a DET DT True\n",
            "taste NOUN NN False\n",
            "of ADP IN True\n",
            "raw ADJ JJ False\n",
            "meat NOUN NN False\n",
            "it PRON PRP True\n",
            "is AUX VBZ True\n",
            "vivid ADJ JJ False\n",
            "red ADJ JJ False\n",
            ". PUNCT . False\n",
            "Tax NOUN NN False\n",
            "Closer ADV RBR False\n",
            "tomin ADP IN False\n",
            "a DET DT True\n",
            "side NOUN NN True\n",
            "I PRON PRP True\n",
            "prefer VERB VBP False\n",
            "ribeyes NOUN NNS False\n",
            "mediumishCooked VERB VBN False\n",
            "toand NOUN NN False\n",
            "rested VERB VBD False\n",
            "10min PROPN NNP False\n",
            "under ADP IN True\n",
            "foil NOUN NN False\n",
            ". PUNCT . False\n",
            "For ADP IN True\n",
            "some DET DT True\n",
            "reason NOUN NN False\n",
            "I PRON PRP True\n",
            "thought VERB VBD False\n",
            "that SCONJ IN True\n",
            "it PRON PRP True\n",
            "was AUX VBD True\n",
            "bowl NOUN NN False\n",
            "of ADP IN True\n",
            "maggots NOUN NNS False\n",
            "If SCONJ IN True\n",
            "it PRON PRP True\n",
            "tastes VERB VBZ False\n",
            "good ADJ JJ False\n",
            "it PRON PRP True\n",
            "* PUNCT NFP False\n",
            "isgood NOUN NN False\n",
            "and CCONJ CC True\n",
            "taste NOUN NN False\n",
            "is AUX VBZ True\n",
            "subjectiveIf PUNCT , False\n",
            "you PRON PRP True\n",
            "did AUX VBD True\n",
            "nt PART RB False\n",
            "taste VERB VB False\n",
            "it PRON PRP True\n",
            "your DET PRP$ True\n",
            "opinion NOUN NN False\n",
            "is AUX VBZ True\n",
            "irrelevant ADJ JJ False\n",
            ". PUNCT . False\n",
            "If SCONJ IN True\n",
            "it PRON PRP True\n",
            "tastes VERB VBZ False\n",
            "good ADJ JJ False\n",
            "it PRON PRP True\n",
            "* PUNCT NFP False\n",
            "isgood NOUN NN False\n",
            "and CCONJ CC True\n",
            "taste NOUN NN False\n",
            "is AUX VBZ True\n",
            "subjectiveIf PUNCT , False\n",
            "you PRON PRP True\n",
            "did AUX VBD True\n",
            "nt PART RB False\n",
            "taste VERB VB False\n",
            "it PRON PRP True\n",
            "your DET PRP$ True\n",
            "opinion NOUN NN False\n",
            "is AUX VBZ True\n",
            "irrelevant ADJ JJ False\n",
            ". PUNCT . False\n",
            "! PUNCT . False\n",
            "[ PUNCT -LRB- False\n",
            "gif](giphy|hQSkQm41brwdYDIGqr|downsized PROPN NNP False\n",
            ") PUNCT -RRB- False\n",
            "Oh INTJ UH False\n",
            "wow INTJ UH False\n",
            "this DET DT True\n",
            "looks VERB VBZ False\n",
            "amazingDoes VERB VBZ False\n",
            "it PRON PRP True\n",
            "taste VERB VBP False\n",
            "like SCONJ IN False\n",
            "the DET DT True\n",
            "center NOUN NN False\n",
            "of ADP IN True\n",
            "a DET DT True\n",
            "Reese PROPN NNP False\n",
            "’s PART POS True\n",
            "peanut NOUN NN False\n",
            "butter NOUN NN False\n",
            "cup PROPN NNP False\n",
            "? PUNCT . False\n",
            "I PRON PRP True\n",
            "have AUX VBP True\n",
            "n’t PART RB True\n",
            "had VERB VBN True\n",
            "either CCONJ CC True\n",
            "as ADV RB True\n",
            "well ADV RB True\n",
            "😂 PUNCT . False\n",
            "Delete VERB VB False\n",
            "the DET DT True\n",
            "sauce NOUN NN False\n",
            "syrup NOUN NN False\n",
            "strawberries NOUN NNS False\n",
            "waffles NOUN NNS False\n",
            "and CCONJ CC True\n",
            "that DET DT True\n",
            "breadcrumbed VERB VBD False\n",
            "thingAll PROPN NNP False\n",
            "too ADV RB True\n",
            "carby ADJ JJ False\n",
            ". PUNCT . False\n",
            "Looks VERB VBZ False\n",
            "so ADV RB True\n",
            "delicious ADJ JJ False\n",
            ". PUNCT . False\n",
            "I PRON PRP True\n",
            "’m VERB VBP True\n",
            "not PART RB True\n",
            "being AUX VBG True\n",
            "facetious ADJ JJ False\n",
            "I PRON PRP True\n",
            "genuinely ADV RB False\n",
            "do AUX VBP True\n",
            "n’t PART RB True\n",
            "understand VERB VB False\n",
            "what PRON WP True\n",
            "the DET DT True\n",
            "ketchup NOUN NN False\n",
            "is AUX VBZ True\n",
            "for ADP IN True\n",
            ". PUNCT . False\n",
            "Shit PROPN NNP False\n",
            "replying VERB VBG False\n",
            "to ADP IN True\n",
            "my DET PRP$ True\n",
            "own ADJ JJ True\n",
            "comment NOUN NN False\n",
            "because SCONJ IN True\n",
            "reddit NOUN NN False\n",
            "by ADP IN True\n",
            "wylinMy NUM CD False\n",
            "initial ADJ JJ False\n",
            "comment NOUN NN False\n",
            "is AUX VBZ True\n",
            "nt PART RB False\n",
            "a DET DT True\n",
            "complaintCarbs NOUN NN False\n",
            "are AUX VBP True\n",
            "tasty ADJ JJ False\n",
            "my DET PRP$ True\n",
            "ninja NOUN NN False\n",
            ". PUNCT . False\n",
            "Oh INTJ UH False\n",
            "my INTJ UH True\n",
            "over ADV RB True\n",
            "at ADP IN True\n",
            "the DET DT True\n",
            "watermelon NOUN NN False\n",
            "in ADP IN True\n",
            "my DET PRP$ True\n",
            "kitchenthis NOUN NN False\n",
            "looks VERB VBZ False\n",
            "absolutely ADV RB False\n",
            "stunning ADJ JJ False\n",
            "yet CCONJ CC True\n",
            "refreshing ADJ JJ False\n",
            ". PUNCT . False\n",
            "Jollof PROPN NNP False\n",
            "is AUX VBZ True\n",
            "a DET DT True\n",
            "dish NOUN NN False\n",
            "from ADP IN True\n",
            "SenegalIm PROPN NNP False\n",
            "Dominican PROPN NNP False\n",
            "so SCONJ IN True\n",
            "I PRON PRP True\n",
            "just ADV RB True\n",
            "made VERB VBD True\n",
            "a DET DT True\n",
            "fusion NOUN NN False\n",
            "of ADP IN True\n",
            "something PRON NN True\n",
            "called VERB VBN False\n",
            "Locrio PROPN NNP False\n",
            "from ADP IN True\n",
            "my DET PRP$ True\n",
            "countryI PROPN NNP False\n",
            "didcups NOUN NNS False\n",
            "of ADP IN True\n",
            "rice NOUN NN False\n",
            "and CCONJ CC True\n",
            "a DET DT True\n",
            "cup NOUN NN False\n",
            "of ADP IN True\n",
            "the DET DT True\n",
            "jollof PROPN NNP False\n",
            "sauceThen PROPN NNP False\n",
            "probably ADV RB False\n",
            "aboutand VERB VBP False\n",
            "half NOUN NN False\n",
            "of ADP IN True\n",
            "water NOUN NN False\n",
            ". PUNCT . False\n",
            "That DET DT True\n",
            "’s VERB VBZ True\n",
            "how ADV WRB True\n",
            "those DET DT True\n",
            "things NOUN NNS False\n",
            "are AUX VBP True\n",
            "labeled VERB VBN False\n",
            "in ADP IN True\n",
            "the DET DT True\n",
            "so NOUN NN True\n",
            "for ADP IN True\n",
            "other ADJ JJ True\n",
            "Americans PROPN NNPS False\n",
            "it PRON PRP True\n",
            "’s VERB VBZ True\n",
            "very ADV RB True\n",
            "clearAt NOUN NN False\n",
            "the DET DT True\n",
            "store NOUN NN False\n",
            "these DET DT True\n",
            "are AUX VBP True\n",
            "labeled VERB VBN False\n",
            "as SCONJ IN True\n",
            "Italian ADJ JJ False\n",
            "sausage NOUN NN False\n",
            "Swiss ADJ JJ False\n",
            "cheese NOUN NN False\n",
            "English ADJ JJ False\n",
            "muffins NOUN NNS False\n",
            "Canadian ADJ JJ False\n",
            "bacon NOUN NN False\n",
            "French ADJ JJ False\n",
            "baguette NOUN NN False\n",
            "etcThe VERB VBP False\n",
            "average ADJ JJ False\n",
            "person NOUN NN False\n",
            "does AUX VBZ True\n",
            "n’t PART RB True\n",
            "know VERB VB False\n",
            "where ADV WRB True\n",
            "the DET DT True\n",
            "styles NOUN NNS False\n",
            "originated VERB VBD False\n",
            "from ADP IN True\n",
            "— PUNCT : False\n",
            "just ADV RB True\n",
            "the DET DT True\n",
            "name NOUN NN True\n",
            ". PUNCT . False\n",
            "I PRON PRP True\n",
            "amI VERB VBD False\n",
            "eat NOUN NN False\n",
            "and CCONJ CC True\n",
            "other ADJ JJ True\n",
            "stuff NOUN NN False\n",
            "😋 PROPN NNP False\n",
            "Perfection NOUN NN False\n",
            "Shove VERB VB False\n",
            "it PRON PRP True\n",
            "in ADP IN True\n",
            "a DET DT True\n",
            "pig NOUN NN False\n",
            "and CCONJ CC True\n",
            "you PRON PRP True\n",
            "get VERB VBP True\n",
            "a DET DT True\n",
            "SeAL NOUN NN False\n",
            "Yep INTJ UH False\n",
            "! PUNCT . False\n",
            "! PUNCT . False\n",
            "Sausage NOUN NN False\n",
            "bacon NOUN NN False\n",
            "eggs NOUN NNS False\n",
            "hash VERB VBP False\n",
            "brown PROPN NNP False\n",
            "Op NOUN NN False\n",
            "did AUX VBD True\n",
            "nt PART RB False\n",
            "make VERB VB True\n",
            "what PRON WP True\n",
            "I PRON PRP True\n",
            "d VERB MD False\n",
            "call VERB VBP True\n",
            "a DET DT True\n",
            "traditional ADJ JJ False\n",
            "phoGarlic NOUN NN False\n",
            "lemon NOUN NN False\n",
            "grass NOUN NN False\n",
            "cilantro NOUN NN False\n",
            "is AUX VBZ True\n",
            "nt PART RB False\n",
            "rightCoriander VERB VB False\n",
            "yea INTJ UH False\n",
            "but CCONJ CC True\n",
            "that DET DT True\n",
            "s VERB VBZ False\n",
            "seedsVietnamese ADJ JJ False\n",
            "use VERB VBP False\n",
            "plenty NOUN NN False\n",
            "of ADP IN True\n",
            "cilantro NOUN NN False\n",
            "in ADP IN True\n",
            "other ADJ JJ True\n",
            "dishes NOUN NNS False\n",
            "but CCONJ CC True\n",
            "not PART RB True\n",
            "to PART TO True\n",
            "wilt VERB VB False\n",
            "in ADP IN True\n",
            "a DET DT True\n",
            "soupLime PROPN NNP False\n",
            "is AUX VBZ True\n",
            "a DET DT True\n",
            "garnish NOUN NN False\n",
            "with ADP IN True\n",
            "bean NOUN NN False\n",
            "sprouts NOUN NNS False\n",
            "and CCONJ CC True\n",
            "basil NOUN NN False\n",
            "adding VERB VBG False\n",
            "it PRON PRP True\n",
            "to ADP IN True\n",
            "the DET DT True\n",
            "broth NOUN NN False\n",
            "while SCONJ IN True\n",
            "cooking NOUN NN False\n",
            "dulls VERB VBZ False\n",
            "itAlso PROPN NNP False\n",
            "completely ADV RB False\n",
            "missing VERB VBG False\n",
            "fish NOUN NN False\n",
            "sauce NOUN NN False\n",
            "roasted VERB VBD False\n",
            "ginger NOUN NN False\n",
            "and CCONJ CC True\n",
            "cardamom NOUN NN False\n",
            "podsUncle PROPN NNP False\n",
            "Roger PROPN NNP False\n",
            "would VERB MD True\n",
            "not PART RB True\n",
            "approve VERB VB False\n",
            "without ADP IN True\n",
            "msgThis DET DT False\n",
            "is AUX VBZ True\n",
            "a DET DT True\n",
            "Jaime PROPN NNP False\n",
            "Oliver PROPN NNP False\n",
            "version NOUN NN False\n",
            "not PART RB True\n",
            "even ADV RB True\n",
            "Gordon PROPN NNP False\n",
            "RamsayHaiyaaaa PROPN NNP False\n",
            "you PRON PRP True\n",
            "can VERB MD True\n",
            "get AUX VB True\n",
            "this DET DT True\n",
            "anywhere ADV RB True\n",
            "why ADV WRB True\n",
            "the DET DT True\n",
            "upvotes NOUN NNS False\n",
            "? PUNCT . False\n",
            "Looks VERB VBZ False\n",
            "more ADJ JJR True\n",
            "like SCONJ IN False\n",
            "a DET DT True\n",
            "cocktopus NOUN NN False\n",
            "to ADP IN True\n",
            "meOtherwise PROPN NNP False\n",
            "that DET DT True\n",
            "s VERB VBZ False\n",
            "a DET DT True\n",
            "small ADJ JJ False\n",
            "turkey NOUN NN False\n",
            ". PUNCT . False\n",
            "Roasted PROPN NNP False\n",
            "Chtlulu PROPN NNP False\n",
            "Yes INTJ UH False\n",
            "recipe NOUN NN False\n",
            ". PUNCT . False\n",
            "Not PART RB True\n",
            "even ADV RB True\n",
            "close ADV RB False\n",
            "to PART TO True\n",
            "huevos PROPN NNP False\n",
            "rancheros NOUN NNS False\n",
            "Good ADJ JJ False\n",
            "size NOUN NN False\n",
            "burgerAll VERB VBD False\n",
            "these DET DT True\n",
            "huge ADJ JJ False\n",
            "overloaded ADJ JJ False\n",
            "burgers NOUN NNS False\n",
            "are AUX VBP True\n",
            "too ADV RB True\n",
            "hard ADJ JJ False\n",
            "to PART TO True\n",
            "eat VERB VB False\n",
            "and CCONJ CC True\n",
            "not PART RB True\n",
            "enjoyableThis DET DT False\n",
            "is AUX VBZ True\n",
            "just ADV RB True\n",
            "right ADJ JJ False\n",
            ". PUNCT . False\n",
            "So ADV RB True\n",
            "purdy ADJ JJ False\n",
            "  SPACE _SP False\n",
            "“ PUNCT `` False\n",
            "I PRON PRP True\n",
            "m VERB VBP False\n",
            "committing VERB VBG False\n",
            "carbicide PROPN NNP False\n",
            "! PUNCT . False\n",
            "” PUNCT '' False\n",
            "RecipeThanks PROPN NNP False\n",
            "Rice PROPN NNP False\n",
            "in ADP IN True\n",
            "a DET DT True\n",
            "burrito NOUN NN False\n",
            "is AUX VBZ True\n",
            "amazing ADJ JJ False\n",
            "Sir PROPN NNP False\n",
            "that DET DT True\n",
            "’s VERB VBZ True\n",
            "clearly ADV RB False\n",
            "a DET DT True\n",
            "duckQuackapus X XX False\n",
            "Nah INTJ UH False\n",
            "it PRON PRP True\n",
            "’s VERB VBZ True\n",
            "a DET DT True\n",
            "duck NOUN NN False\n",
            "What PRON WP True\n",
            "ails VERB VBZ False\n",
            "that DET DT True\n",
            "bun NOUN NN False\n",
            "? PUNCT . False\n",
            "Lmao INTJ UH False\n",
            "I PRON PRP True\n",
            "thought VERB VBD False\n",
            "the DET DT True\n",
            "same ADJ JJ True\n",
            "thing NOUN NN False\n",
            "I PRON PRP True\n",
            "’m VERB VBP True\n",
            "sorry ADJ JJ False\n",
            "you PRON PRP True\n",
            "feel VERB VBP False\n",
            "that SCONJ IN True\n",
            "wayHope VERB VBP False\n",
            "you PRON PRP True\n",
            "still ADV RB True\n",
            "have AUX VBP True\n",
            "a DET DT True\n",
            "great ADJ JJ False\n",
            "day NOUN NN False\n",
            "😊 PROPN NNP False\n",
            "Had AUX VBD True\n",
            "this DET DT True\n",
            "for ADP IN True\n",
            "dinner NOUN NN False\n",
            "a DET DT True\n",
            "few ADJ JJ True\n",
            "days NOUN NNS False\n",
            "agoYum ADJ JJ False\n",
            ". PUNCT . False\n",
            "Ichiran PROPN NNP False\n",
            "? PUNCT . False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "noun_tokens = []\n",
        "doc = nlp(comment_str)\n",
        "for token in doc:\n",
        "  if(token.pos_ == 'NOUN'):\n",
        "    noun_tokens.append(token.text)"
      ],
      "metadata": {
        "id": "aEgrVYttvhAC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "txtsentiment = pipeline(\"sentiment-analysis\", truncation=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FsBsoTMulY2O",
        "outputId": "8537ec41-fe54-4101-f282-ce11da75c830"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "noun_str = ' '.join(noun_tokens)\n",
        "noun_str"
      ],
      "metadata": {
        "id": "H2uxUFEDlY4v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(txtsentiment(noun_str))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iLZCtALknkhS",
        "outputId": "00f65484-ca64-4e72-f012-19f0b3db0bef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'label': 'NEGATIVE', 'score': 0.9966613054275513}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "c7L5tpPUoCWC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "vectorizer = CountVectorizer(ngram_range=(1,2),stop_words='english')\n",
        "tfidf_vec = vectorizer.fit([comment_str])"
      ],
      "metadata": {
        "id": "XM9fKvWp3-mk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf_vectors = vectorizer.get_feature_names_out()"
      ],
      "metadata": {
        "id": "H286ICmb42jC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf_vectors[:100]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IumZOYQv42l1",
        "outputId": "b9631394-846d-48f2-ae48-411a7ec3ed39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['0325', '0325 ds_c', '05', '05 17', '0jncztdhu8o',\n",
              "       '0jncztdhu8o fbclid', '0roper', '0roper good', '10', '10 10',\n",
              "       '10 im', '10 lot', '10 love', '10 pizza', '10 state', '10 using',\n",
              "       '10 whats', '100', '100 notice', '1000060068',\n",
              "       '1000060068 defaultskuid', '100ml', '100ml dry', '105g',\n",
              "       '105g heavy', '10k', '10k calories', '10min', '10min foil', '10oz',\n",
              "       '10oz beefroughlyherecourse', '10pizza', '10pizza box', '10th',\n",
              "       '10th dentist', '10x', '10x percentage', '11', '11 30pmi',\n",
              "       '11 https', '115g', '115g melted', '11am', '11am 1pm', '11yrs',\n",
              "       '11yrs people', '12', '12 15min', '12 count', '12hour',\n",
              "       '12hour flight', '13', '13 99', '1385', '1385 format', '13a',\n",
              "       '13a tierhunt', '13am', '13am looks', '140g', '140g sugar', '145g',\n",
              "       '145g corn', '15', '15 im', '15 laptop', '15 wow', '150c',\n",
              "       '150c fanpour', '150g', '150g dark', '150k', '150k smokers', '155',\n",
              "       '155 seasoned', '15g', '15g butter', '15g lime', '15min',\n",
              "       '15min depending', '15min slicing', '16', '16 enjoy', '160g',\n",
              "       '160g dates', '17', '17 platanos', '170c', '170c mix', '175g',\n",
              "       '175g self', '176', '176 449k', '1790', '1790 6am',\n",
              "       '1790 horrible', '1790 img', '1791', '1791 img', '1791 need'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Code below is used to build a custom model for keyword extraction using \n",
        "# spacy Noun words and distilbert embedding for them\n",
        "# then finding cosine similarity. \n",
        "# This is widely a quoted method from BERTopic model. Link already given\n",
        "# This code is NOT COMPLETE!\n",
        "from transformers import AutoModel, AutoTokenizer\n",
        "model_name = \"distilbert-base-uncased\"\n",
        "model = AutoModel.from_pretrained(model_name)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252,
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        },
        "id": "rjRprrLQ7EA_",
        "outputId": "d49846f9-67f1-4f3e-caf3-f3e23a785094"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "47286928bbfe445ea045b4f41873a25b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/256M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "462d10f97c9c44bdb17538ac660d44cb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_projector.weight', 'vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_projector.bias', 'vocab_transform.bias', 'vocab_layer_norm.bias']\n",
            "- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f1848119add3405eb2b777d85dc64886"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1081307bc0fa4d948f3a76dd5de2d0e0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/455k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "82d332721f584b9d839de983f364a5b7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This code fails in Colab due to large embedding\n",
        "main_tokens = tokenizer(noun_tokens, padding=True, return_tensors=\"pt\")\n",
        "mainword_embeddings = model(**main_tokens)[\"pooler_output\"]\n"
      ],
      "metadata": {
        "id": "tIdffQsV7Kj9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "nFk5x4Iv7WH3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}